Securing the Future of AI: A Deep Dive into Model Context Protocol (MCP) Security
Securing the Future of AI: A Deep Dive into Model Context Protocol (MCP) Security
Speaker - Anshu Gupta
Session Brief:
As AI systems become more powerful and context-aware, ensuring secure and reliable interaction with Large Language Models (LLMs) is paramount. Model Context Protocol (MCP) introduces a standardized interface that governs how models are prompted, contextualized, and deployed in real-world applications.
This session explores the emerging security landscape of MCP, covering the potential risks introduced by context injection, prompt leakage, prompt chaining abuse, and data exfiltration through contextual inputs. Attendees will learn best practices for hardening MCP implementations across enterprise LLM stacks—whether proprietary or using API-based access like OpenAI, Anthropic, or Cohere.
From input validation and sandboxing to contextual trust boundaries, this session offers a strategic and technical roadmap to secure LLM interactions using MCP.
Key Takeaways:
Understand the structure and components of the Model Context Protocol (MCP)
Explore key threat vectors in context-driven LLM workflows
Learn security best practices for MCP usage, including prompt hygiene, red teaming, and audit logging
Dive into real-world attack scenarios such as indirect prompt injection and model manipulation via context chaining
Recommendations for integrating MCP with existing AppSec and SOC workflows
Prerequisites:
Familiarity with Large Language Models, AI security principles, and API-based architecture.